



'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class 							a__:
			a_	: str									=      MBartConfig
			a_	: Tuple									=      {}
			a_	: int									=      '''gelu'''






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase=13					,   _UpperCAmelCase=7					,   _UpperCAmelCase=True					,   _UpperCAmelCase=False					,   _UpperCAmelCase=99					,   _UpperCAmelCase=32					,   _UpperCAmelCase=2					,   _UpperCAmelCase=4					,   _UpperCAmelCase=37					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=20					,   _UpperCAmelCase=2					,   _UpperCAmelCase=1					,   _UpperCAmelCase=0					,   )							->							List[Any]:
					snake_case__	     =parent
					snake_case__	     =batch_size
					snake_case__	     =seq_length
					snake_case__	     =is_training
					snake_case__	     =use_labels
					snake_case__	     =vocab_size
					snake_case__	     =hidden_size
					snake_case__	     =num_hidden_layers
					snake_case__	     =num_attention_heads
					snake_case__	     =intermediate_size
					snake_case__	     =hidden_dropout_prob
					snake_case__	     =attention_probs_dropout_prob
					snake_case__	     =max_position_embeddings
					snake_case__	     =eos_token_id
					snake_case__	     =pad_token_id
					snake_case__	     =bos_token_id






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =ids_tensor([self.batch_size, self.seq_length - 1]					,   self.vocab_size						)
					snake_case__	     =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)					,   1						)
					snake_case__	     =tf.concat([input_ids, eos_tensor]					,   axis=1						)

					snake_case__	     =ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size						)

					snake_case__	     =self.config_cls(
					    vocab_size=self.vocab_size					,   d_model=self.hidden_size					,   encoder_layers=self.num_hidden_layers					,   decoder_layers=self.num_hidden_layers					,   encoder_attention_heads=self.num_attention_heads					,   decoder_attention_heads=self.num_attention_heads					,   encoder_ffn_dim=self.intermediate_size					,   decoder_ffn_dim=self.intermediate_size					,   dropout=self.hidden_dropout_prob					,   attention_dropout=self.attention_probs_dropout_prob					,   max_position_embeddings=self.max_position_embeddings					,   eos_token_ids=[2]					,   bos_token_id=self.bos_token_id					,   pad_token_id=self.pad_token_id					,   decoder_start_token_id=self.pad_token_id					,   **self.config_updates					,   )
					snake_case__	     =prepare_mbart_inputs_dict(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)
					return config, inputs_dict






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Union[str, Any]:
					snake_case__	     =TFMBartModel(config=_UpperCAmelCase						).get_decoder()
					snake_case__	     =inputs_dict['input_ids']

					snake_case__	     =input_ids[:1, :]
					snake_case__	     =inputs_dict['attention_mask'][:1, :]
					snake_case__	     =inputs_dict['head_mask']
					snake_case__	     =1

					# first forward pass
					snake_case__	     =model(_UpperCAmelCase					,   attention_mask=_UpperCAmelCase					,   head_mask=_UpperCAmelCase					,   use_cache=_UpperCAmelCase						)

					snake_case__     ,	snake_case__	     =outputs.to_tuple()
					snake_case__	     =past_key_values[1]






def 				a  (     UpperCamelCase_    :    Any      ,					UpperCamelCase_    :    List[str]      ,					UpperCamelCase_    :    Tuple      ,					UpperCamelCase_    :    Dict=None      ,					UpperCamelCase_    :    Dict=None      ,					UpperCamelCase_    :    Tuple=None      ,					UpperCamelCase_    :    Tuple=None      ,					UpperCamelCase_    :    Any=None      ,					)       ->	Optional[int]:
		if attention_mask is None:
				snake_case__	     =tf.cast(tf.math.not_equal(UpperCamelCase_      ,					config.pad_token_id			)      ,					tf.inta			)
		if decoder_attention_mask is None:
				snake_case__	     =tf.concat(
				    [
				        tf.ones(decoder_input_ids[:, :1].shape      ,					dtype=tf.inta			),
				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]      ,					config.pad_token_id			)      ,					tf.inta			),
				    ]      ,					axis=-1      ,					)
		if head_mask is None:
				snake_case__	     =tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				snake_case__	     =tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				snake_case__	     =tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}

@require_tf
class 							a__( snake_case__   ,      snake_case__   ,      unittest.TestCase     ):
			a_	: int									=      (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
			a_	: str									=      (TFMBartForConditionalGeneration,) if is_tf_available() else ()
			a_	: Tuple									=      (
			    {
			        '''conversational''': TFMBartForConditionalGeneration,
			        '''feature-extraction''': TFMBartModel,
			        '''summarization''': TFMBartForConditionalGeneration,
			        '''text2text-generation''': TFMBartForConditionalGeneration,
			        '''translation''': TFMBartForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			a_	: List[str]									=      True
			a_	: List[Any]									=      False
			a_	: Union[str, Any]									=      False






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Tuple:
					if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
							# Exception encountered when calling layer '...'
							return True

					return False






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =TFMBartModelTester(self						)
					snake_case__	     =ConfigTester(self					,   config_class=_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[int]:
					self.config_tester.run_common_tests()






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase						)





@require_sentencepiece
@require_tokenizers
@require_tf
class 							a__( unittest.TestCase     ):
			a_	: Any									=      [
			    ''' UN Chief Says There Is No Military Solution in Syria''',
			]
			a_	: List[Any]									=      [
			    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
			]
			a_	: Optional[int]									=      '''facebook/mbart-large-en-ro'''






			@cached_property
			def 						_lowercase  (     self						)							->							List[Any]:
					return AutoTokenizer.from_pretrained(self.model_name						)






			@cached_property
			def 						_lowercase  (     self						)							->							str:
					snake_case__	     =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
					return model






			def 						_lowercase  (     self					,   **_UpperCAmelCase						)							->							Optional[Any]:
					snake_case__	     =self.translate_src_text(**_UpperCAmelCase						)
					self.assertListEqual(self.expected_text					,   _UpperCAmelCase						)






			def 						_lowercase  (     self					,   **_UpperCAmelCase						)							->							str:
					snake_case__	     =self.tokenizer(self.src_text					,   **_UpperCAmelCase					,   return_tensors='tf'						)
					snake_case__	     =self.model.generate(
					    model_inputs.input_ids					,   attention_mask=model_inputs.attention_mask					,   num_beams=2						)
					snake_case__	     =self.tokenizer.batch_decode(_UpperCAmelCase					,   skip_special_tokens=_UpperCAmelCase						)
					return generated_words






			@slow
			def 						_lowercase  (     self						)							->							str:
					self._assert_generated_batch_equal_expected()









'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
			if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
			from .pipeline_unclip import UnCLIPPipeline
			from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
			from .text_proj import UnCLIPTextProjModel




